SoroushVahidi/scidocs huggingface dataset:
Humanoid robot HRP-3
(2004)

In this paper, the development of humanoid robot HRP-3 is presented. HRP-3, which stands for Humanoid Robotics Platform-3, is a human-size humanoid robot developed as the succeeding model of HRP-2. One of features of HRP-3 is that its main mechanical and structural components are designed to prevent the penetration of dust or spray. Another is that its wrist and hand are newly designed to improve manipulation. Software for a humanoid robot in a real environment is also improved. We also include information on mechanical features of HRP-3 and together with the newly developed hand. Also included are the technologies implemented in HRP-3 prototype. Electrical features and some experimental results using HRP-3 are also presented.
Modeling and Simulation of Electric and Hybrid Vehicles
(2007)

This paper discusses the need for modeling and simulation of electric and hybrid vehicles. Different modeling methods such as physics-based Resistive Companion Form technique and Bond Graph method are presented with powertrain component and system modeling examples. The modeling and simulation capabilities of existing tools such as Powertrain System Analysis Toolkit (PSAT), ADvanced VehIcle SimulatOR (ADVISOR), PSIM, and Virtual Test Bed are demonstrated through application examples. Since power electronics is indispensable in hybrid vehicles, the issue of numerical oscillations in dynamic simulations involving power electronics is briefly addressed
A Matlab-based modeling and simulation package for electric and hybrid electric vehicle design
(1999)

This paper discusses a simulation and modeling package developed at Texas A&M University, V-Elph 2.01. VElph facilitates in-depth studies of electric vehicle (EV) and hybrid EV (HEV) configurations or energy management strategies through visual programming by creating components as hierarchical subsystems that can be used interchangeably as embedded systems. V-Elph is composed of detailed models of four major types of components: electric motors, internal combustion engines, batteries, and support components that can be integrated to model and simulate drive trains having all electric, series hybrid, and parallel hybrid configurations. V-Elph was written in the Matlab/Simulink graphical simulation language and is portable to most computer platforms. This paper also discusses the methodology for designing vehicle drive trains using the V-Elph package. An EV, a series HEV, a parallel HEV, and a conventional internal combustion engine (ICE) driven drive train have been designed using the simulation package. Simulation results such as fuel consumption, vehicle emissions, and complexity are compared and discussed for each vehicle.
PSIM-based modeling of automotive power systems: conventional, electric, and hybrid electric vehicles
(2004)

Automotive manufacturers have been taking advantage of simulation tools for modeling and analyzing various types of vehicles, such as conventional, electric, and hybrid electric vehicles. These simulation tools are of great assistance to engineers and researchers to reduce product-development cycle time, improve the quality of the design, and simplify the analysis without costly and time-consuming experiments. In this paper, a modeling tool that has been developed to study automotive systems using the power electronics simulator (PSIM) software is presented. PSIM was originally made for simulating power electronic converters and motor drives. This user-friendly simulation package is able to simulate electric/electronic circuits; however, it has no capability for simulating the entire system of an automobile. This paper discusses the PSIM validity as an automotive simulation tool by creating module boxes for not only the electrical systems, but also the mechanical, energy-storage, and thermal systems of the vehicles. These modules include internal combustion engines, fuel converters, transmissions, torque couplers, and batteries. Once these modules are made and stored in the library, the user can make the car model either a conventional, an electric, or a hybrid vehicle at will, just by dragging and dropping onto a schematic blank page.
Principles of object-oriented modeling and simulation with Modelica 2.1
(2003)

Why should wait for some days to get or receive the principles of object oriented modeling and simulation with modelica 2 1 pdf book that you order? Why should you take it if you can get the faster one? You can find the same book that you order right here. This is it the book that you can receive directly after purchasing. This principles of object oriented modeling and simulation with modelica 2 1 pdf is well known book in the world, of course many people will try to own it. Why don't you become the first? Still confused with the way?
ADVISOR 2.1: a user-friendly advanced powertrain simulation using a combined backward/forward approach
(1999)

-ADVISOR 2.1 is the latest version of the National Renewable Energy Laboratory’s advanced vehicle simulator. It was first developed in 1994 to support the U.S. Department of Energy hybrid propulsion system program, and is designed to be accurate, fast, flexible, easily sharable, and easy to use. This paper presents the model, focusing on its combination of forwardand backward-facing simulation approaches, and evaluates the model in terms of its design goals. ADVISOR predicts acceleration time to within 0.7% and energy use on the demanding US06 to within 0.6% for an underpowered series hybrid vehicle (0-100 km/h in 20 s). ADVISOR simulates vehicle performance on standard driving cycles between 2.6 and 8.0 times faster than a representative forward-facing vehicle model. Due in large part to ADVISOR’s powerful graphical user interface and Web presence, over 800 users have downloaded ADVISOR from 45 different countries. Many of these users have contributed their own component data to the ADVISOR library.
Marital status and health: United States, 1999-2002.
(2004)

OBJECTIVE
This report presents prevalence estimates by marital status for selected health status and limitations, health conditions, and health risk behaviors among U.S. adults, using data from the 1999-2002 National Health Interview Surveys (NHIS).


METHODS
Data for the U.S. civilian noninstitutionalized population were collected using computer-assisted personal interviews (CAPI). The household response rate for the NHIS was 88.7%. This report is based on a total of 127,545 interviews with sample adults aged 18 years and over, representing an overall response rate of 72.4% for the 4 years combined. Statistics were age-adjusted to the 2000 U.S. standard population. Marital status categories shown in this report are: married, widowed, divorced or separated, never married, and living with a partner.


RESULTS
Regardless of population subgroup (age, sex, race, Hispanic origin, education, income, or nativity) or health indictor (fair or poor health, limitations in activities, low back pain, headaches, serious psychological distress, smoking, or leisure-time physical inactivity), married adults were generally found to be healthier than adults in other marital status categories. Marital status differences in health were found in each of the three age groups studied (18-44 years, 45-64 years, and 65 years and over), but were most striking among adults aged 18-44 years. The one negative health indicator for which married adults had a higher prevalence was overweight or obesity. Married adults, particularly men, had high rates of overweight or obesity relative to adults in other marital status groups across most population subgroups studied. Never married adults were among the least likely to be overweight or obese.
Synthetic therapeutic peptides: science and market.
(2010)

The decreasing number of approved drugs produced by the pharmaceutical industry, which has been accompanied by increasing expenses for R&D, demands alternative approaches to increase pharmaceutical R&D productivity. This situation has contributed to a revival of interest in peptides as potential drug candidates. New synthetic strategies for limiting metabolism and alternative routes of administration have emerged in recent years and resulted in a large number of peptide-based drugs that are now being marketed. This review reports on the unexpected and considerable number of peptides that are currently available as drugs and the chemical strategies that were used to bring them into the market. As demonstrated here, peptide-based drug discovery could be a serious option for addressing new therapeutic challenges.
H-Ras transfers from B to T cells via tunneling nanotubes
(2013)

Lymphocytes form cell–cell connections by various mechanisms, including intercellular networks through actin-supported long-range plasma membrane (PM) extensions, termed tunneling nanotubes (TNTs). In this study, we tested in vitro whether TNTs form between human antigen-presenting B cells and T cells following cell contact and whether they enable the transfer of PM-associated proteins, such as green fluorescent protein (GFP)-tagged H-Ras (GFP-H-Ras). To address this question, we employed advanced techniques, including cell trapping by optical tweezers and live-cell imaging by 4D spinning-disk confocal microscopy. First, we showed that TNTs can form after optically trapped conjugated B and T cells are being pulled apart. Next, we determined by measuring fluorescence recovery after photobleaching that GFP-H-Ras diffuses freely in the membrane of TNTs that form spontaneously between B and T cells during coculturing. Importantly, by 4D time-lapse imaging, we showed that GFP-H-Ras-enriched PM patches accumulate at the junction between TNTs and the T-cell body and subsequently transfer to the T-cell surface. Furthermore, the PM patches adopted by T cells were enriched for another B-cell-derived transmembrane receptor, CD86. As predicted, the capacity of GFP-H-Ras to transfer between B and T cells, during coculturing, was dependent on its normal post-transcriptional lipidation and consequent PM anchorage. In summary, our data indicate that TNTs connecting B and T cells provide a hitherto undescribed route for the transfer of PM patches containing, for example, H-Ras from B to T cells.
SciMATE: A Novel MapReduce-Like Framework for Multiple Scientific Data Formats
(2012)

Despite the popularity of MapReduce, there are several obstacles to applying it for developing scientific data analysis applications. Current MapReduce implementations require that data be loaded into specialized file systems, like the Hadoop Distributed File System (HDFS), whereas with rapidly growing size of scientific datasets, reloading data in another file system or format is not feasible. We present a framework that allows scientific data in different formats to be processed with a MapReduce like API. Our system is referred to as SciMATE, and is based on the MATE system developed at Ohio State. SciMATE is developed as a customizable system, which can be adapted to support processing on any of the scientific data formats. We have demonstrated the functionality of our system by creating instances that can be processing NetCDF and HDF5 formats as well as flat-files. We have also implemented three popular data mining applications and have evaluated their execution with each of the three instances of our system.
Assessing the Impact of Real-time Ridesharing on Urban Traffic using Mobile Phone Data
(2015)

Recently, smart-phone based technology has enabled ridesharing services to match customers making similar trips in realtime for a reduced rate and minimal inconvenience. But what are the impacts of such services on city-wide congestion? The answer lies in whether or not ridesharing adds to vehicle traffic by diverting non-driving trips like walking, transit, or cycling, or reduces vehicle traffic by diverting trips otherwise made in private, single occupancy cars or taxis. This research explores the impact of rideshare adoption on congestion using mobile phone data. We extract average daily origin-destination (OD) trips from mobile phone records and estimate the proportions of these trips made by auto and other non-auto travelers. Next, we match spatially and temporally similar trips, and assume a range of adoption rates for auto and non-auto users, in order to distill rideshare vehicle trips. Finally, for several adoption scenarios, we evaluate the impacts of congestion network-wide.
An overview of online trust: Concepts, elements, and implications
(2005)

Lack of trust has been repeatedly identified as one of the most formidable barriers to people for engaging in e-commerce, involving transactions in which financial and personal information is submitted to merchants via the Internet. The future of e-commerce is tenuous without a general climate of online trust. Building consumer trust on the Internet presents a challenge for online merchants and is a research topic of increasing interest and importance. This paper provides an overview of the nature and concepts of trust from multi-disciplinary perspectives, and it reviews relevant studies that investigate the elements of online trust. Also, a framework of trust-inducing interface design features articulated from the existing literature is presented. The design features were classified into four dimensions, namely (1) graphic design, (2) structure design, (3) content design, and (4) social-cue design. By applying the design features identified within this framework to e-commerce web site interfaces, online merchants might then anticipate fostering optimal levels of trust in their customers. 2003 Elsevier Ltd. All rights reserved.
Exploring compassion: a meta-analysis of the association between self-compassion and psychopathology.
(2012)

Compassion has emerged as an important construct in studies of mental health and psychological therapy. Although an increasing number of studies have explored relationships between compassion and different facets of psychopathology there has as yet been no systematic review or synthesis of the empirical literature. We conducted a systematic search of the literature on compassion and mental health. We identified 20 samples from 14 eligible studies. All studies used the Neff Self Compassion Scale (Neff, 2003b). We employed meta-analysis to explore associations between self-compassion and psychopathology using random effects analyses of Fisher's Z correcting for attenuation arising from scale reliability. We found a large effect size for the relationship between compassion and psychopathology of r=-0.54 (95% CI=-0.57 to -0.51; Z=-34.02; p<.0001). Heterogeneity was significant in the analysis. There was no evidence of significant publication bias. Compassion is an important explanatory variable in understanding mental health and resilience. Future work is needed to develop the evidence base for compassion in psychopathology, and explore correlates of compassion and psychopathology.
Autonomous boids
(2006)

The classical work of bird-like objects of Reynolds [1] simulates polarized motion of groups of oriented particles, bird-like objects, or simply boids. To do this, three steering vectors are introduced. Cohesion is the tendency of boids to stay in the center of the flock, alignment smoothes their velocities to similar values, and separation helps them to avoid mutual collisions. If no impetus is introduced the boids wander somewhat
Evaluation of Quranic text retrieval system based on manually indexed topics
(2011)

This paper investigates the effectiveness of a state of the art information retrieval (IR) system in the verse retrieval problem for Quranic text. The evaluation is based on manually indexed topics of the Quran that provides both the queries and the relevance judgments. Furthermore, the system is evaluated in both Malay and English environment. The performance of the system is measured based on the MAP, the precision at 1, 5 and 10, and the MRR scores. The results of the evaluation are promising, showing the IR system has many potential for the Quranic text retrieval.
Lyapunov-based control for switched power converters
(1990)

The fundamental properties, such as passivity or incremental passivity, of the network elements making up a switched power converter are examined. The nominal open-loop operation of a broad class of such converters is shown to be stable in the large via a Lyapunov argument. The obtained Lyapunov function is then shown to be useful for designing globally stabilizing controls that include adaptive schemes for handling uncertain nominal parameters. Numerical simulations illustrate the application of this control approach in DC-DC converters.<<ETX>>
Pancreas Segmentation in Abdominal CT Scan: A Coarse-to-Fine Approach
(2016)

Deep neural networks have been widely adopted for automatic organ segmentation from CT-scanned images. However, the segmentation accuracy on some small organs (e.g., the pancreas) is sometimes below satisfaction, arguably because deep networks are easily distracted by the complex and variable background region which occupies a large fraction of the input volume. In this paper, we propose a coarse-to-fine approach to deal with this problem. We train two deep neural networks using different regions of the input volume. The first one, the coarse-scaled model, takes the entire volume as its input. It is used for roughly locating the spatial position of the pancreas. The second one, the fine-scaled model, only sees a small input region covering the pancreas, thus eliminating the background noise and providing more accurate segmentation especially around the boundary areas. At the testing stage, we first use the coarse-scaled model to roughly locate the pancreas, then adopt the fine-scaled model to refine the initial segmentation in an iterative manner to obtain increasingly better segmentation. We evaluate our algorithm on the NIH pancreas segmentation dataset with 82 volumes, and outperform the state-of-theart [18] by more than 4%, measured by the Dice-Sørensen Coefficient (DSC). In addition, we report 62.43% DSC for our worst case, significantly better than 34.11% reported in [18].
UNFOLD: a memory-efficient speech recognizer using on-the-fly WFST composition
(2017)

Accurate, real-time Automatic Speech Recognition (ASR) requires huge memory storage and computational power. The main bottleneck in state-of-the-art ASR systems is the Viterbi search on a Weighted Finite State Transducer (WFST). The WFST is a graph-based model created by composing an Acoustic Model (AM) and a Language Model (LM) offline. Offline composition simplifies the implementation of a speech recognizer as only one WFST has to be searched. However, the size of the composed WFST is huge, typically larger than a Gigabyte, resulting in a large memory footprint and memory bandwidth requirements.
 In this paper, we take a completely different approach and propose a hardware accelerator for speech recognition that composes the AM and LM graphs on-the-fly. In our ASR system, the fully-composed WFST is never generated in main memory. On the contrary, only the subset required for decoding each input speech fragment is dynamically generated from the AM and LM models. In addition to the direct benefits of this on-the-fly composition, the resulting approach is more amenable to further reduction in storage requirements through compression techniques.
 The resulting accelerator, called UNFOLD, performs the decoding in real-time using the compressed AM and LM models, and reduces the size of the datasets from more than one Gigabyte to less than 40 Megabytes, which can be very important in small form factor mobile and wearable devices.
 Besides, UNFOLD improves energy-efficiency by orders of magnitude with respect to CPUs and GPUs. Compared to a state-of-the-art Viterbi search accelerators, the proposed ASR system outperforms by providing 31x reduction in memory footprint and 28% energy savings on average.
Visual-Inertial-Semantic Scene Representation for 3D Object Detection
(2017)

We describe a system to detect objects in three-dimensional space using video and inertial sensors (accelerometer and gyrometer), ubiquitous in modern mobile platforms from phones to drones. Inertials afford the ability to impose class-specific scale priors for objects, and provide a global orientation reference. A minimal sufficient representation, the posterior of semantic (identity) and syntactic (pose) attributes of objects in space, can be decomposed into a geometric term, which can be maintained by a localization-and-mapping filter, and a likelihood function, which can be approximated by a discriminatively-trained convolutional neural network The resulting system can process the video stream causally in real time, and provides a representation of objects in the scene that is persistent: Confidence in the presence of objects grows with evidence, and objects previously seen are kept in memory even when temporarily occluded, with their return into view automatically predicted to prime re-detection.
Computational Intelligence in Sports: A Systematic Literature Review
(2018)

Recently, data mining studies are being successfully conducted to estimate several parameters in a variety of domains. Data mining techniques have attracted the attention of the information industry and society as a whole, due to a large amount of data and the imminent need to turn it into useful knowledge. However, the effective use of data in some areas is still under development, as is the case in sports, which in recent years, has presented a slight growth; consequently, many sports organizations have begun to see that there is a wealth of unexplored knowledge in the data extracted by them. Therefore, this article presents a systematic review of sports data mining. Regarding years 2010 to 2018, 31 types of research were found in this topic. Based on these studies, we present the current panorama, themes, the database used, proposals, algorithms, and research opportunities. Our findings provide a better understanding of the sports data mining potentials, besides motivating the scientific community to explore this timely and interesting topic.
Design of a distributed energy-efficient clustering algorithm for heterogeneous wireless sensor networks
(2006)

The clustering Algorithm is a kind of key technique used to reduce energy consumption. It can increase the scalability and lifetime of the network. Energy-efficient clustering protocols should be designed for the characteristic of heterogeneous wireless sensor networks. We propose and evaluate a new distributed energy-efficient clustering scheme for heterogeneous wireless sensor networks, which is called DEEC. In DEEC, the cluster-heads are elected by a probability based on the ratio between residual energy of each node and the average energy of the network. The epochs of being cluster-heads for nodes are different according to their initial and residual energy. The nodes with high initial and residual energy will have more chances to be the cluster-heads than the nodes with low energy. Finally, the simulation results show that DEEC achieves longer lifetime and more effective messages than current important clustering protocols in heterogeneous environments. 2006 Elsevier B.V. All rights reserved.
Reading the mind in cartoons and stories: an fMRI study of ‘theory of mind’ in verbal and nonverbal tasks
(2000)

Previous functional imaging studies have explored the brain regions activated by tasks requiring 'theory of mind'--the attribution of mental states. Tasks used have been primarily verbal, and it has been unclear to what extent different results have reflected different tasks, scanning techniques, or genuinely distinct regions of activation. Here we report results from a functional magnetic resonance imaging study (fMRI) involving two rather different tasks both designed to tap theory of mind. Brain activation during the theory of mind condition of a story task and a cartoon task showed considerable overlap, specifically in the medial prefrontal cortex (paracingulate cortex). These results are discussed in relation to the cognitive mechanisms underpinning our everyday ability to 'mind-read'.